MAGRIT - INRIA

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MAGRIT

Medical Imaging

Interventional neuro-radiology

In interventional neuro-radiology, therapeutic tools are inserted within the arteries, up to the lesion through a catheter under the control of various image modalities: 2DSA (digital subtracted angiography), 3DRA (3D rotational angiography), MRI. Our team focuses on the use of multimodal systems to help therapeutic interventions. Effective clinical applications are developed in collaborations with the Hospital of Nancy and bore on the segmentation of arterioveinous malformations and the concept of augmented fluoroscopy to help surgeon's gesture.

A methodology for validating a new imaging modality with respect to a gold standard Imagerie (M.O. Berger, R. Anxionnat, E. Kerrien), MICCAI 2004 .
Model of a vascular C-arm for 3D augmented fluoroscopy in interventional radiology (S. Gorges, E. Kerrien, M.O. Berger, Y. Trousset, J. Pescatore, MICCAI 05)
3D Augmented Fluoroscopy in Interventional Neuroradiology: Precision Assessment and First Evaluation on Clinical Cases Gorges S., Kerrien E., Berger M.-O., Trousset Y., Pescatore J., Anxionnat R., Picard L., Bracard S. In Workshop on Augmented environments for Medical Imaging and Computer-aided Surgery - AMI-ARCS 2006 (held in conjunction with MICCAI'06)

Realistic patient-based simulators for planning the embolization of intracranial aneurisms

A simulation tool of the interventional act, adapted to the patient's anatomy and physiology, would help to plan the coil placement, train the surgeon, and improve the medical training to the technique. Research on this subject are conducted in collaboration with the Alcove, now SHACRA INRIA group. Our goal is to provide in-vivo models of the patient's organs, and in particular a precise geometric model of the arterial wall with the aim to use these models for real time simulation.

The SIMPLE INRIA project aimed at developing methods to simulate coil deployment in an intracranial aneurysm, running in real time and adaptable to any patient data. See further details and a poster (in french) about this work.
Refining the 3D surface of blood vessels from a reduced set of 2D DSA images, workshop AMI-ARCS 2008,
Interactive simulation of embolization coils: modeling and experimental simulation, MICCAI 2008,
Robust RANSAC based blood vessel segmentation, SPIE Medical Imaging, 2012,
Local implicit modeling of blood vessels for interactive simulation, MICCAI 2012

Augmented reality for deformable organs

This work is a collaboration with the Shacra team (Lille, Strabourg). We aim at augmenting in real time laparoscopic views in the context of hepatic surgery. Tracking of the liver is done thanks to a bio-mechanical model of the liver guided by image features extracted and tracked on the video at the surface of the organ. Specific models which best suit the considered organs, such as a vascularized model of the liver, have been introduced in this framework. Experiments show that the in-depth localization errors is less than 6mm, and thus below the safety margin required by surgery.

Deformation-based Augmented Reality for Hepatic Surgery. Nazim Haouchine; Jérémie Dequidt; Marie-Odile Berger; Stéphane Cotin. In Medicine Meets Virtual Reality, MMVR 20, Feb 2013, San Diego.
Image-guided Simulation of Heterogeneous Tissue Deformation For Augmented Reality during Hepatic Surgery Nazim Haouchine; Jérémie Dequidt; Igor Peterlik ; Erwan Kerrien ; Marie-Odile Berger; Stéphane Cotin. ISMAR - IEEE International Symposium on Mixed and Augmented Reality 2013, Oct 2013, Adelaide, Australia.
Monocular 3D Reconstruction and Augmentation of Elastic Surfaces with Self-occlusion Handling, TVCG 2015

Image-Driven simulation and the IDeaS ANR project

IDeaS is a fundamental research program targeted at per-operative guidance for interventional radiology procedures. Our main goal is to provide effective solutions for the two main drawbacks of interventional radiology procedures, namely: reduce radiation exposure and provide a fully 3D and interactive visual feedback during the procedure. To do so, our project relies on an original combination of computer vision algorithms and interactive physics-based medical simulation. Computer vision algorithms extract relevant information (like the actual projected shape of the guide-wire at any given time) from X-ray images, allowing adjusting the simulation to real data. Conversely, computer-based simulation is used as a sophisticated and trustful predictor for an improved initialization of computer vision tracking algorithms.